Optimizing military planners course of action decision making

Abstract

Military planners are faced with ever-increasing constraints, obstacles, and priority readjustments during the course of action (COA) development. This upward trajectory places a more demanding cognitive workload on decision makers, which only further complicates their jobs. An effort to mediate workload is currently ongoing in the armed services through the development of systems that assist the planners in COA decision-making. I conducted an experiment that evaluates three different strategies for route selection within the Tool for Multi-Objective Planning and Asset Routing (TMPLAR) framework to aid decision makers through the use of route filtering (via sliders) and clustering (via scatter-gather) to support the selection of high utility routes while reducing route selection latency and associated workload. Study participants went through multiple levels of COA planning in a game-like scenario-driven computer application. The results suggest that filtering through slider configurations tools will enhance users to select the better routes that reflect the commander’s intent compared to the other two strategies. Also, this study delivered feedback on usability and perceived workload from using TMPLAR. The research achieved at improving our understanding of military decision making to assist military leaders in using supervisory control of an optimizer for accurate, efficient route planning.